
GCN [1]} propagates node features by defining spectral graph convolutions on graph data. In the experiments, we adopt a 3-layer GCN model.

SAGE [2]} updates node features by sampling neighbor nodes and aggregating their information. SAGE-GCN, SAGE-mean and SAGE-pool are used as student models.

GAT [3]} introduces the attention mechanism to automatically assign weights to nodes' neighbors. In the PPI dataset, we adopt GAT with different layers and dimensions for the teacher model and the student model.

